import pandas as pd

df = pd.read_csv('Chocolate Sales.csv')

df.head()

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1094 entries, 0 to 1093
Data columns (total 6 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   Sales Person   1094 non-null   object
 1   Country        1094 non-null   object
 2   Product        1094 non-null   object
 3   Date           1094 non-null   object
 4   Amount         1094 non-null   object
 5   Boxes Shipped  1094 non-null   int64 
dtypes: int64(1), object(5)
memory usage: 51.4+ KB

df['Amount'] = df['Amount'].str.replace('[\$,]', '', regex=True)
df['Amount'] = pd.to_numeric(df['Amount'], errors='coerce')

<>:1: SyntaxWarning: invalid escape sequence '\$'
<>:1: SyntaxWarning: invalid escape sequence '\$'
C:\Users\pablo\AppData\Local\Temp\ipykernel_6652\4229683131.py:1: SyntaxWarning: invalid escape sequence '\$'
  df['Amount'] = df['Amount'].str.replace('[\$,]', '', regex=True)

df['Date']= pd.to_datetime(df['Date'], errors='coerce')

C:\Users\pablo\AppData\Local\Temp\ipykernel_6652\1852781647.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  df['Date']= pd.to_datetime(df['Date'], errors='coerce')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1094 entries, 0 to 1093
Data columns (total 6 columns):
 #   Column         Non-Null Count  Dtype         
---  ------         --------------  -----         
 0   Sales Person   1094 non-null   object        
 1   Country        1094 non-null   object        
 2   Product        1094 non-null   object        
 3   Date           1094 non-null   datetime64[ns]
 4   Amount         1094 non-null   int64         
 5   Boxes Shipped  1094 non-null   int64         
dtypes: datetime64[ns](1), int64(2), object(3)
memory usage: 51.4+ KB

df.describe()

# Filtrar columnas object
obj_cols = df.select_dtypes(include='object').columns

# Recorrer columnas y mostrar desglose
for col in obj_cols:
    print(f'{"="*50}')
    print(f'Columna: {col}')
    print(f'Cardinalidad: {df[col].nunique()} valores únicos')
    print('Valores más frecuentes:')
    print(df[col].value_counts())  # Los 10 más frecuentes
    print('\n')

==================================================
Columna: Sales Person
Cardinalidad: 25 valores únicos
Valores más frecuentes:
Sales Person
Kelci Walkden          54
Brien Boise            53
Van Tuxwell            51
Beverie Moffet         50
Dennison Crosswaite    49
Oby Sorrel             49
Ches Bonnell           48
Karlen McCaffrey       47
Gigi Bohling           47
Curtice Advani         46
Kaine Padly            45
Madelene Upcott        45
Marney O'Breen         45
Barr Faughny           43
Jehu Rudeforth         43
Roddy Speechley        43
Gunar Cockshoot        43
Mallorie Waber         41
Jan Morforth           39
Andria Kimpton         39
Husein Augar           38
Dotty Strutley         36
Wilone O'Kielt         34
Rafaelita Blaksland    34
Camilla Castle         32
Name: count, dtype: int64


==================================================
Columna: Country
Cardinalidad: 6 valores únicos
Valores más frecuentes:
Country
Australia      205
India          184
USA            179
UK             178
Canada         175
New Zealand    173
Name: count, dtype: int64


==================================================
Columna: Product
Cardinalidad: 22 valores únicos
Valores más frecuentes:
Product
50% Dark Bites          60
Eclairs                 60
Smooth Sliky Salty      59
White Choc              58
Drinking Coco           56
Spicy Special Slims     54
Organic Choco Syrup     52
After Nines             50
85% Dark Bars           50
Fruit & Nut Bars        50
Milk Bars               49
Peanut Butter Cubes     49
99% Dark & Pure         49
Almond Choco            48
Raspberry Choco         48
Orange Choco            47
Mint Chip Choco         45
Manuka Honey Choco      45
Caramel Stuffed Bars    43
70% Dark Bites          42
Baker's Choco Chips     41
Choco Coated Almonds    39
Name: count, dtype: int64

from plotnine import ggplot, aes, geom_line, geom_point, labs, theme_minimal, scale_x_datetime
import numpy as np

# Suponiendo que 'df' ya está cargado

# Agrupar datos por mes
df_time = df.groupby(df['Date'].dt.to_period('M'))['Amount'].sum().reset_index()
df_time['Date'] = df_time['Date'].dt.to_timestamp()  # Convertir de Period a Timestamp

(ggplot(df_time, aes(x='Date', y='Amount')) +
  geom_line(color='blue') +
  geom_point(color='red') +
  labs(title='Ventas totales mensuales', x='Fecha', y='Ventas ($)') +
  scale_x_datetime(date_breaks='1 month', date_labels='%b %Y') +
  theme_minimal())

C:\Users\pablo\anaconda3\Lib\site-packages\mizani\breaks.py:448: FutureWarning: Passing the width as the parameter has been deprecated and will not work in a future version. Use breaks_date(width="4 years")

import dash
from dash import dcc, html
import plotly.express as px


app = dash.Dash(__name__)

app.layout = html.Div([
    html.H1("Dashboard de Ventas de Chocolate"),
    dcc.Dropdown(
        id='country-dropdown',
        options=[{'label': c, 'value': c} for c in df['Country'].unique()],
        value=df['Country'].unique()[0],
        multi=False
    ),
    dcc.Graph(id='ventas-tiempo')
])

@app.callback(
    dash.dependencies.Output('ventas-tiempo', 'figure'),
    [dash.dependencies.Input('country-dropdown', 'value')]
)
def update_graph(selected_country):
    filtered = df[df['Country'] == selected_country].copy()
    
    # Agrupar por mes correctamente
    filtered['Month'] = filtered['Date'].dt.to_period('M')
    time_data = (
        filtered.groupby('Month')['Amount']
        .sum()
        .reset_index()
    )
    time_data['Month'] = time_data['Month'].dt.to_timestamp()

    fig = px.line(
        time_data,
        x='Month',
        y='Amount',
        title=f'Ventas mensuales en {selected_country}',
        markers=True
    )
    fig.update_layout(xaxis_title="Fecha", yaxis_title="Ventas ($)")
    
    fig.write_html('dash-evo.html')
    return fig
    

if __name__ == '__main__':
    app.run(debug=True, port= 8051)

df['Month'] = df['Date'].dt.to_period('M').astype(str)  # Ejemplo: '2022-01'

# (Opcional) Agrupar para sumar ventas por país, producto y mes
agg = df.groupby(['Month', 'Product']).agg({
    'Amount': 'sum',
    'Boxes Shipped': 'sum'
}).reset_index()

# Gráfico animado
fig = px.scatter(
    agg,
    x="Amount",
    y="Boxes Shipped",
    animation_frame="Month",
    size="Amount",
    color="Product",
    size_max=60,
    range_x=[0, agg['Amount'].max()*1.1],
    range_y=[0, agg['Boxes Shipped'].max()*1.1],
    title="Evolución mensual de ventas de chocolate"
)
fig.show()
fig.write_html('evomensual.html')

from plotnine import ggplot, aes, geom_line, geom_point, geom_boxplot, labs, theme_minimal, coord_flip

(
    ggplot(df)
    + aes( x = "Country",
         y = "Amount")
    + geom_boxplot()
     
#    + theme_classic()
    
)

df_grouped = df.groupby(['Country', 'Product'], as_index=False).agg({
    'Amount': 'sum',
    'Boxes Shipped': 'mean'  # o 'mean', según lo que tenga sentido
})
df_grouped

fig = px.treemap(
    df_grouped,
    path=['Country', 'Product'],
    values='Amount',
    color='Boxes Shipped',  # ← escala de color basada en Amount
    color_continuous_scale='Blues',  # o 'Viridis', 'RdBu', 'YlGnBu', etc.
    title='Ventas por país y producto en relación al número de cajas enviadas (Treemap)'
)

fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))  # más limpio
fig.show()
fig.write_html('treemap.html')

import matplotlib.pyplot as plt
import seaborn as sns
# Tabla dinámica
pivot = df.pivot_table(values='Amount', index='Country', columns='Product', aggfunc='sum', fill_value=0)

plt.figure(figsize=(18,8))
sns.heatmap(pivot, annot=False, cmap='YlGnBu')
plt.title('Ventas totales por país y producto')
plt.show()

<Figure size 1800x800 with 0 Axes>

df_grouped2 = df.groupby(['Country', 'Sales Person'], as_index=False).agg({
    'Boxes Shipped': ['sum', 'mean']
})
df_grouped2.columns = ['Country', 'Sales Person', 'Boxes_Sum', 'Boxes_Mean']
df_grouped2

fig = px.sunburst(df_grouped2, 
                  title=("Boxes shipped por pais y sales person"),
                  height=800,
                  width=800,
                  path=['Country', 
                        'Sales Person'], 
                  values='Boxes_Sum',
                  color='Boxes_Mean')
fig.show()
fig.write_html('sunburst.html')

country_mapping = {
    'USA': 'United States of America',
    'UK': 'United Kingdom'
    # Puedes añadir más si es necesario
}

# Aplicar el mapeo
df['Country'] = df['Country'].replace(country_mapping)

# Agrupar ventas totales por país (ya mapeados)
ventas_pais = df.groupby('Country')['Amount'].sum().reset_index()
ventas_pais

import requests

url = 'https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/world-countries.json'
geojson_countries = requests.get(url).json()

# Crear un diccionario con ventas por país
ventas_dict = ventas_pais.set_index('Country')['Amount'].to_dict()

# Añadir la venta total a cada feature del geojson
for feature in geojson_countries['features']:
    country_name = feature['properties']['name']
    feature['properties']['Amount'] = ventas_dict.get(country_name, 0)

import folium

# Centrar el mapa en el mundo
m = folium.Map(location=[20, 0], zoom_start=2)

# Añadir la capa coroplética
folium.Choropleth(
    geo_data=geojson_countries,
    data=ventas_pais,
    columns=['Country', 'Amount'],
    key_on='feature.properties.name',   # Este campo depende del GeoJSON que uses
    fill_color='YlOrRd',
    fill_opacity=0.7,
    line_opacity=0.2,
    legend_name='Ventas totales de chocolate ($)',
    nan_fill_color='white'
).add_to(m)

# Mostrar el mapa (en Jupyter) o guardarlo en HTML
m.save('ventas_chocolate_mundo.html')

folium.GeoJson(
    geojson_countries,
    name="Países",
    tooltip=folium.GeoJsonTooltip(
        fields=["name","Amount"],
        aliases=["País:","Ventas totales: "],
        localize=True
    ),
    style_function=lambda feature: {
        'fillColor': 'transparent',
        'color': 'black',  # Borde negro (puedes quitarlo si quieres)
        'weight': 1,
        'fillOpacity': 0
    }
).add_to(m)

<folium.features.GeoJson at 0x22a72575970>

m

m.save('mapa.html')

	Date	Amount	Boxes Shipped
count	1094	1094.000000	1094.000000
mean	2022-05-03 09:04:56.160877568	5652.308044	161.797989
min	2022-01-03 00:00:00	7.000000	1.000000
25%	2022-03-02 00:00:00	2390.500000	70.000000
50%	2022-05-11 00:00:00	4868.500000	135.000000
75%	2022-07-04 00:00:00	8027.250000	228.750000
max	2022-08-31 00:00:00	22050.000000	709.000000
std	NaN	4102.442014	121.544145

	Country	Product	Amount	Boxes Shipped
0	Australia	50% Dark Bites	89222	198.875000
1	Australia	70% Dark Bites	39354	233.666667
2	Australia	85% Dark Bars	38479	150.000000
3	Australia	99% Dark & Pure	54908	122.727273
4	Australia	After Nines	27769	130.285714
...	...	...	...	...
127	USA	Peanut Butter Cubes	33628	172.714286
128	USA	Raspberry Choco	83524	136.090909
129	USA	Smooth Sliky Salty	45493	165.000000
130	USA	Spicy Special Slims	25214	173.125000
131	USA	White Choc	67221	120.400000

	Country	Sales Person	Boxes_Sum	Boxes_Mean
0	Australia	Andria Kimpton	1317	188.142857
1	Australia	Barr Faughny	657	109.500000
2	Australia	Beverie Moffet	2209	276.125000
3	Australia	Brien Boise	1442	131.090909
4	Australia	Camilla Castle	1073	153.285714
...	...	...	...	...
145	USA	Oby Sorrel	1045	149.285714
146	USA	Rafaelita Blaksland	534	106.800000
147	USA	Roddy Speechley	1494	186.750000
148	USA	Van Tuxwell	788	131.333333
149	USA	Wilone O'Kielt	482	68.857143

	Sales Person	Country	Product	Date	Amount	Boxes Shipped
0	Jehu Rudeforth	UK	Mint Chip Choco	04-Jan-22	$5,320	180
1	Van Tuxwell	India	85% Dark Bars	01-Aug-22	$7,896	94
2	Gigi Bohling	India	Peanut Butter Cubes	07-Jul-22	$4,501	91
3	Jan Morforth	Australia	Peanut Butter Cubes	27-Apr-22	$12,726	342
4	Jehu Rudeforth	UK	Peanut Butter Cubes	24-Feb-22	$13,685	184

	Country	Amount
0	Australia	1137367
1	Canada	962899
2	India	1045800
3	New Zealand	950418
4	United Kingdom	1051792
5	United States of America	1035349